Instructions to use lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_ with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large") model = PeftModel.from_pretrained(base_model, "lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_: direct link, hf CLI and curl.
- Browser
- Download file 19 MB
-
https://huggingface.co/lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_/resolve/main/adapter_model.bin
- Command line
-
hf download hf://lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/lorahub/flan_t5_large-sciq_Direct_Question_Closed_Book_/resolve/main/adapter_model.bin
19 MB
- Xet hash:
- 4a1122d837fb685323cb964c9d671d5f855f2f9d7ade644125b4cf26dbf4b311
- Size of remote file:
- 19 MB
- SHA256:
- 70d53980a8c417ba16fb87a8a098e4b08c4fa8698aafb80a06920fb80cda3d1a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.